SoftPatch: Unsupervised Anomaly Detection with Noisy Data
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an inevitable problem in real-world anomaly detection but is seldom discussed. This paper considers label-level noise …